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awesome-3D-point-cloud-attacks
List of state of the art papers, code, and other resources
https://github.com/cuge1995/awesome-3D-point-cloud-attacks
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Attacks
- Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers.
- Adversarial point perturbations on 3D objects.
- PointCloud Saliency Maps.
- PU-GAN: a Point Cloud Upsampling Adversarial Network.
- 3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions.
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- Adversarial Autoencoders for Compact Representations of 3D Point Clouds.
- Physically Realizable Adversarial Examples for LiDAR Object Detection.
- AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds.
- ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds.
- Robustness of 3D Deep Learning in an Adversarial Setting.
- Nudge Attacks on Point-Cloud DNNs.
- Geometry-aware generation of adversarial point clouds.
- Adversarial Objects Against LiDAR Based Autonomous Driving Systems.
- Minimal Adversarial Examples for Deep Learning on 3D Point Clouds.
- LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks.
- Robust Adversarial Objects against Deep Learning Models.
- Self-Robust 3D Point Recognition via Gather-Vector Guidance.
- On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks.
- Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures.
- Geometric Adversarial Attacks and Defenses on 3D Point Clouds.
- Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models.
- Object Removal Attacks on LiDAR-based 3D Object Detectors.
- On the Adversarial Robustness of 3D Point Cloud Classification.
- Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving.
- Fooling LiDAR Perception via Adversarial Trajectory Perturbation.
- PointBA: Towards Backdoor Attacks in 3D Point Cloud.
- 3D Adversarial Attacks Beyond Point Cloud.
- Explainability-Aware One Point Attack for Point Cloud Neural Networks.
- Adversarial Attack by Limited Point Cloud Surface Modifications.
- PointBA: Towards Backdoor Attacks in 3D Point Cloud.
- A Backdoor Attack Against 3D Point Cloud Classifiers.
- Generating Unrestricted 3D Adversarial Point Clouds.
- Local Aggressive Adversarial Attack of 3D point Cloud.
- Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification.
- Attacking Point Cloud Segmentation with Color-only Perturbation
- Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network
- Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks.
- Boosting 3D Adversarial Attacks with Attacking On Frequency
- Shape-invariant 3D Adversarial Point Clouds.
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- Generating Adversarial Surfaces via Band‐Limited Perturbations
- Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving.
- Robust Adversarial Objects against Deep Learning Models.
- Shape-invariant 3D Adversarial Point Clouds.
- Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers.
- Adversarial point perturbations on 3D objects.
- Nudge Attacks on Point-Cloud DNNs.
- Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models.
- Object Removal Attacks on LiDAR-based 3D Object Detectors.
- On the Adversarial Robustness of 3D Point Cloud Classification.
- Boosting 3D Adversarial Attacks with Attacking On Frequency
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Defenses
- The art of defense: letting networks fool the attacker.
- DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense.
- IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration.
- PointCutMix: Regularization Strategy for Point Cloud Classification.
- PointGuard: Provably Robust 3D Point Cloud Classification.
- Defense-pointnet: Protecting pointnet against adversarial attacks.
- LPF-Defense: 3D Adversarial Defense based on Frequency Analysis.
- Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients.
- Improving Adversarial Robustness of 3D Point Cloud Classification Models
- Ada3Diff: Defending against 3D Adversarial Point Clouds via Adaptive Diffusion
- IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration.
- Defense-pointnet: Protecting pointnet against adversarial attacks.
- LPF-Defense: 3D Adversarial Defense based on Frequency Analysis.
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